mirror of
https://github.com/danny-avila/LibreChat.git
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78 lines
2.7 KiB
JavaScript
78 lines
2.7 KiB
JavaScript
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const {
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ChainStepExecutor,
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LLMPlanner,
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PlanOutputParser,
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PlanAndExecuteAgentExecutor
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} = require('langchain/experimental/plan_and_execute');
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const { LLMChain } = require('langchain/chains');
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const { ChatAgent, AgentExecutor } = require('langchain/agents');
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const { BufferMemory, ChatMessageHistory } = require('langchain/memory');
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const {
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate
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} = require('langchain/prompts');
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const DEFAULT_STEP_EXECUTOR_HUMAN_CHAT_MESSAGE_TEMPLATE = `{chat_history}
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Previous steps: {previous_steps}
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Current objective: {current_step}
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{agent_scratchpad}
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You may extract and combine relevant data from your previous steps when responding to me.`;
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const PLANNER_SYSTEM_PROMPT_MESSAGE_TEMPLATE = [
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`Let's first understand the problem and devise a plan to solve the problem.`,
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`Please output the plan starting with the header "Plan:"`,
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`and then followed by a numbered list of steps.`,
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`Please make the plan the minimum number of steps required`,
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`to answer the query or complete the task accurately and precisely.`,
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`Your steps should be general, and should not require a specific method to solve a step. If the task is a question,`,
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`the final step in the plan must be the following: "Given the above steps taken,`,
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`please respond to the original query."`,
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`At the end of your plan, say "<END_OF_PLAN>"`
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].join(' ');
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const PLANNER_CHAT_PROMPT = /* #__PURE__ */ ChatPromptTemplate.fromPromptMessages([
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/* #__PURE__ */ SystemMessagePromptTemplate.fromTemplate(PLANNER_SYSTEM_PROMPT_MESSAGE_TEMPLATE),
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/* #__PURE__ */ HumanMessagePromptTemplate.fromTemplate(`{input}`)
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]);
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const initializePAEAgent = async ({ tools: _tools, model: llm, pastMessages, ...rest }) => {
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//removed currentDateString
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const tools = _tools.filter((tool) => tool.name !== 'self-reflection');
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const memory = new BufferMemory({
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chatHistory: new ChatMessageHistory(pastMessages),
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// returnMessages: true, // commenting this out retains memory
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memoryKey: 'chat_history',
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humanPrefix: 'User',
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aiPrefix: 'Assistant',
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inputKey: 'input',
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outputKey: 'output'
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});
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const plannerLlmChain = new LLMChain({
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llm,
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prompt: PLANNER_CHAT_PROMPT,
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memory
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});
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const planner = new LLMPlanner(plannerLlmChain, new PlanOutputParser());
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const agent = ChatAgent.fromLLMAndTools(llm, tools, {
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humanMessageTemplate: DEFAULT_STEP_EXECUTOR_HUMAN_CHAT_MESSAGE_TEMPLATE
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});
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const stepExecutor = new ChainStepExecutor(
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AgentExecutor.fromAgentAndTools({ agent, tools, memory, ...rest })
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);
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return new PlanAndExecuteAgentExecutor({
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planner,
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stepExecutor
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});
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};
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module.exports = {
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initializePAEAgent
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};
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